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Classroom discourse is a core medium of instruction --- analyzing it can provide a window into teaching and learning as well as driving the development of new tools for improving instruction. We introduce the largest dataset of mathematics classroom transcripts available to researchers, and demonstrate how this data can help improve instruction. The dataset consists of 1,660 45-60 minute long 4th and 5th grade elementary mathematics observations collected by the National Center for Teacher Effectiveness (NCTE) between 2010-2013. The anonymized transcripts represent data from 317 teachers across 4 school districts that serve largely historically marginalized students. The transcripts come with rich metadata, including turn-level annotations for dialogic discourse moves, classroom observation scores, demographic information, survey responses and student test scores. We demonstrate that our natural language processing model, trained on our turn-level annotations, can learn to identify dialogic discourse moves and these moves are correlated with better classroom observation scores and learning outcomes. This dataset opens up several possibilities for researchers, educators and policymakers to learn about and improve K-12 instruction.
Education is one of the most important public goods provided by modern governments. Yet governments worldwide seldom perform well in the sector. This raises the question: why do governments preside over poor education quality? This article answers this question with evidence from Tanzania. Using data from surveys, administrative reports, and policy documents, it analyzes changing goals of education policy and associated impacts on access and learning over time. The main finding is that learn- ing has not always been the goal of schooling in Tanzania. Furthermore, for decades the government rationed access to both primary and secondary schooling for ideological reasons. These past policy choices partially explain contemporary poor outcomes in education. This article increase our understanding of the politics of education in low-income states. It also provides a corrective against the common assumption that governments always seek to maximize the provision of public goods and services for political gain.
An increasing share of new teachers enter the profession through alternative certification programs. While these programs increase teacher supply in areas facing critical shortages, they also increase instability in local teacher labor markets via high teacher turnover. A fundamental question is what effect these programs have on student achievement over the long run. To address this question, I study Teach For America (TFA) teachers working in New York City (NYC) between 2012 and 2019. This research brief reports on three key findings. First, I document five-year cumulative retention rates and find that, as expected, retention is lower for TFA teachers (25%) than for other NYC teachers working in similar schools (43%). Second, I estimate within-teacher returns to experience using a teacher fixed effects strategy and find that TFA teachers who continue teaching through year five improve at double the rate of the average NYC teacher. Third, I model the joint relationship between turnover and performance over time and find that the TFA performance advantage is large enough to offset turnover costs. I conclude that the net effect of TFA hiring on student achievement is positive in the short and long run.
We examine the labor supply decisions of substitute teachers – a large, on-demand market with broad shortages and inequitable supply. In 2018, Chicago Public Schools implemented a targeted bonus program designed to reduce unfilled teacher absences in largely segregated Black schools with historically low substitute coverage rates. Using a regression discontinuity design, we find that incentive pay substantially improved coverage equity and raised student achievement. Changes in labor supply were concentrated among Black and Hispanic substitutes from nearby neighborhoods with experience in incentive schools. Wage elasticity estimates suggest incentives would need to be 50% of daily wages to close fill-rate gaps.
This simulation study examines the characteristics of the Explanatory Item Response Model (EIRM) when estimating treatment effects when compared to classical test theory (CTT) sum and mean scores and item response theory (IRT)-based theta scores. Results show that the EIRM and IRT theta scores provide generally equivalent bias and false positive rates compared to CTT scores and superior calibration of standard errors under model misspecification. Analysis of the statistical power of each method reveals that the EIRM and IRT theta scores provide a marginal benefit to power and are more robust to missing data than other methods when parametric assumptions are met and provide a substantial benefit to power under heteroskedasticity, but their performance is mixed under other conditions. The methods are illustrated with an empirical data application examining the causal effect of an elementary school literacy intervention on reading comprehension test scores and demonstrates that the EIRM provides a more precise estimate of the average treatment effect than the CTT or IRT theta score approaches. Tradeoffs of model selection and interpretation are discussed.
Recent public discussions and legal decisions suggest that school segregation will remain persistent in the United States, but increased transparency may help monitor spending across schools. These circumstances revive an old question: is it possible to achieve an educational system that is separate but equal—or better—in terms of spending? This question motivates further understanding the measurement of spending progressivity and its association with segregation. Focusing on economic disadvantage, we compare two commonly-used measures of spending progressivity: exposure-based and slope-based. We show that each measure is predicated on different assumptions about the progressivity of within-school resource allocations, and that they are theoretically linked through segregation. We empirically examine school spending progressivity and its properties using nationwide school spending data from the 2018-19 school year. Consistent with our theory, the exposure-based measure is the slope-based measure shrunk inversely by economic school segregation. This property makes more segregated school districts look more progressive on the exposure-based measure, representing a seemingly “separate but better” relationship. However, we show that this provocative pattern may be reversed by relatively modest poor-versus-nonpoor differences in unobserved parental contributions. We discuss implications for the measurement of progressivity, and for theory on public educational investments broadly.
We examine the state of the U.S. K-12 teaching profession over the last half century by compiling nationally representative time-series data on four interrelated constructs: professional prestige, interest among students, preparation for entry, and job satisfaction. We find a consistent and dynamic pattern across every measure: a rapid decline in the 1970s, a swift rise in the 1980s, relative stability for two decades, and a sustained drop beginning around 2010. The current state of the teaching profession is at or near its lowest levels in 50 years. We identify and explore a range of factors that might explain these historical patterns including education funding, teacher pay, outside opportunities, unionism, barriers to entry, working conditions, accountability, autonomy, and school shootings.
Districts nationwide have revised their educator evaluation systems, increasing the frequency with which administrators observe and evaluate teacher instruction. Yet, limited insight exists on the role of evaluator feedback for instructional improvement. Relying on unique observation-level data, we examine the alignment between evaluator and teacher assessments of teacher instruction and the potential consequences for teacher productivity and mobility. We show that teachers and evaluators typically rate teacher performance similarly during classroom observations, but with significant variability in teacher-evaluator ratings. While teacher performance improves across multiple classroom observations, evaluator ratings likely overstate productivity improvements among the lowest-performing teachers. Evaluators, but not teachers, systematically rate teacher performance lower in classrooms serving higher concentrations of economically disadvantaged students. And while teacher performance improves when evaluators provide more critical feedback about teacher instruction, teachers receiving critical feedback may seek alternative teaching assignments in schools with less critical evaluation settings. We discuss the implications of these findings for the design, implementation and impact of educator evaluation systems.
Current public pension funding policy has arguably failed on both theoretical and empirical grounds. The traditional actuarial approach elides the risk-return tradeoff at the heart of finance economics and has resulted in steadily rising contribution rates, instead of a sustainable steady state. We propose an economic reformulation of funding policy based on steady-state analysis of the fundamental equations of motion for pension asset and liability growth, incorporating both an expected return on risky assets and a low-risk discount rate for liabilities. Our steady-state result simultaneously conveys the benefit of risky investment and the cost of the associated risk. We integrate our analysis into a simple social welfare function to re-examine the basis for pre-funding and elucidate the net benefits of using risky assets to defray contributions. We also formally derive a family of transition policies for convergence to the expected steady state. We illustrate how the parameters of our proposed policy can be adjusted to manage the tradeoff between long-run contribution rate risk and short-term responsiveness. We believe our analysis provides the basis for reformulating contribution policy in a way that better supports sustainability and coherently conveys the tradeoffs consistent with finance economics.
Books shape how children learn about society and norms, in part through representation of different characters. We introduce new artificial intelligence methods for systematically converting images into data and apply them, along with text analysis methods, to measure the representation of race, gender, and age in award-winning children’s books from the past century. We find that more characters with darker skin color appear over time, but the most influential books persistently depict a greater proportion of light-skinned characters than other books, even after conditioning on race; we also find that children are depicted with lighter skin than adults. Relative to their growing share of the U.S. population, Black and Latinx people are underrepresented in these same books, while White males are overrepresented. Over time, females are increasingly present but appear less often in text than in images, suggesting greater symbolic inclusion in pictures than substantive inclusion in stories. We then report empirical evidence for predictions about the supply of and demand for representation that would generate these patterns. On the demand side, we show that people consume books that center their own identities. On the supply side, we document higher prices for books that center non-dominant social identities and fewer copies of these books in libraries that serve predominantly White communities. Lastly, we show that the types of children’s books purchased in a neighborhood are related to local political beliefs.